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Answer: Analyze the current data sources to identify inefficiencies, then refactor them to use more efficient data retrieval and integration strategies, such as implementing incremental refresh and partitioning.
Option B is the most effective approach because it directly addresses the root cause of the performance issues by optimizing the data sources themselves, rather than just treating the symptoms. Implementing strategies like incremental refresh and partitioning can significantly improve performance without necessarily increasing costs or violating compliance policies. Option A may not be feasible due to compliance and data governance requirements that could prevent consolidation. Option C would negatively impact the real-time analytics capability, which is critical for the financial services company. Option D might provide a temporary performance boost but does not solve the underlying inefficiencies in data management.
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As a Microsoft Fabric Analytics Engineer Associate, you are tasked with optimizing a semantic model that is experiencing performance degradation due to the complexity of managing a large number of data sources. The model is critical for real-time analytics in a financial services company that operates under strict compliance and data governance policies. Which of the following approaches would BEST optimize the performance of the semantic model while ensuring compliance and minimizing operational costs? (Choose one)
A
Consolidate all data sources into a single, high-capacity data warehouse to simplify management and improve performance, despite the potential increase in initial setup costs.
B
Analyze the current data sources to identify inefficiencies, then refactor them to use more efficient data retrieval and integration strategies, such as implementing incremental refresh and partitioning.
C
Disable all automatic processing features and switch to manual processing to reduce the load on the system, accepting the trade-off of delayed data availability for analytics.
D
Upgrade the hardware resources allocated to the semantic model processing engine to handle the complexity, without making any changes to the data sources themselves.